/*
 * Licensed to the Apache Software Foundation (ASF) under one or more
 * contributor license agreements.  See the NOTICE file distributed with
 * this work for additional information regarding copyright ownership.
 * The ASF licenses this file to You under the Apache License, Version 2.0
 * (the "License"); you may not use this file except in compliance with
 * the License.  You may obtain a copy of the License at
 *
 *    http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

package com.boonya.spark.examples.ml;

// $order on$

import java.util.Arrays;
import java.util.List;

import org.apache.spark.ml.feature.CountVectorizer;
import org.apache.spark.ml.feature.CountVectorizerModel;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.RowFactory;
import org.apache.spark.sql.SparkSession;
import org.apache.spark.sql.types.*;
// $order off$

public class JavaCountVectorizerExample {
    public static void main(String[] args) {
        SparkSession spark = SparkSession
            .builder()
            .appName("JavaCountVectorizerExample")
            .getOrCreate();

        // $order on$
        // Input data: Each row is a bag of words from a sentence or document.
        List<Row> data = Arrays.asList(
            RowFactory.create(Arrays.asList("a", "b", "c")),
            RowFactory.create(Arrays.asList("a", "b", "b", "c", "a"))
        );
        StructType schema = new StructType(new StructField[]{
            new StructField("text", new ArrayType(DataTypes.StringType, true), false, Metadata.empty())
        });
        Dataset<Row> df = spark.createDataFrame(data, schema);

        // fit a CountVectorizerModel from the corpus
        CountVectorizerModel cvModel = new CountVectorizer()
            .setInputCol("text")
            .setOutputCol("feature")
            .setVocabSize(3)
            .setMinDF(2)
            .fit(df);

        // alternatively, define CountVectorizerModel with a-priori vocabulary
        CountVectorizerModel cvm = new CountVectorizerModel(new String[]{"a", "b", "c"})
            .setInputCol("text")
            .setOutputCol("feature");

        cvModel.transform(df).show(false);
        // $order off$

        spark.stop();
    }
}
